(373ac) Optimal Gas Treatment and Coal Blending for Reduced Emissions in Power Plants

Guerras, L. S., University of Salamanca
Martín, M., University of Salamanca
Coal based power plants are still responsible for a fair share of the power production worldwide. However, current trends towards clean and sustainable energy have led to an effort towards reducing SO2 and NOx emissions and water consumption. Modelling has been widely used to evaluate the performance of the facilities and the units. The different works have focused on one the four main sections of the power plants: the boiler, the power cycle, the cooling units and the flue gas treatment section. The flue gas section has received less attention in the literature, focusing mainly on carbon capture. Emission control includes the evaluation of the boiler operation. Two approaches have been used to reduce emissions: i) the implementation of measures to reduce the formation of those species by controlling the combustion at the boiler. ii) the installation of end of trail gas treatment. Together with CO2, the emission of NOx and SO2 must be limited. There are several alternative denitrification (DeNOx) technologies such as catalytic or non-catalytic processes, as well as wet (Limestone Forced Oxidation. LSFO) and dry systems (LSD) for desulfurization (DeSOx). The removal yield and cost of each technology depends on the operating conditions and the addition of chemicals such as ammonia, CaO or CaCO3 [1]. The actual selection of technologies and their relative position in the treatment chain depends on the coal composition and emission limits.

In this work, a framework is developed to select among different denitrification and desulfurization technologies and their relative position along the flue gas treatment chain of a power plant for the future selection of technologies in a local power plant. It consists of a two-stage procedure. The first stage corresponds to the pre-screening of the technologies available based on industrial know-how. The second one consists of formulating a superstructure model [2] of alternative technologies from the boiler to the discharge of the flue gas involving denitrifier, desulfurizer and particle removal. Surrogate models for each of the units are developed based on experimental and industrial data providing a flexible framework to evaluate and design the flue gas treatment process. Once the plant is installed, it may have to process different coals. Price fluctuations in coal, supply agreements, social reasons and the ever-changing policies can result in the interest or need to process different types of coal. Coal blending is a well-known topic aiming at selecting the proper mixture of coal types to meet sulphur content and optimize the combustion properties [3]. However, the blending studies in the literature are limited to the addition of process constraints on the composition of the feed to the mathematical formulation of the problem. In this work we have used this approach to formulate a coal blending problem that includes a detail model of the technologies responsible for flue gas processing as a decision making tool to help select the coal blend among three coal types, imported, national and crude tar coal that are typically used in a local power plant nearby.

The optimization suggests the use of electrostatic precipitation, followed by catalytic NOx removal and dry SO2 removal. Next, a coal blending problem has also been solved. When only treatment costs are considered, the use of imported coal is recommended, but an increase of 4% in its price can change the decision into the use of national coal. If the energy within the coal is added to the objective function, crude tar coal is included in the blend and imported coal is used to maintain the emissions within limits. LSFO is the selected technology for SO2 removal.


Staff of “La Robla” power plant for their support and fruitful conversations.



http://northlondonheatandpower.london/documents/141117_NLWA_Flue_Gas_Treatment_Technology_Options_Consultation_V1.pdf Last accessed March 2018

[2] Grossmann, I. E, Caballero J. A, Yeomans H. Advances in mathematical programming for the synthesis of process systems. Latin American Applied Research, 2000. 30:263-284

[3] Shih JS, Frey HC.. Coal blending optimization under uncertainty. Eur. J. Oper. Res., 1995; 83, 3(22): 452-465